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Sensors 2017, 17(9), 1955;

An Energy-Aware Runtime Management of Multi-Core Sensory Swarms

Division of Computer Science and Engineering, Chonbuk National University, 567 Baekje-daero, deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea
Department of Electrical and Computer Engineering, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon-si 16499, Korea
Author to whom correspondence should be addressed.
Received: 6 July 2017 / Revised: 14 August 2017 / Accepted: 22 August 2017 / Published: 24 August 2017
(This article belongs to the Section Sensor Networks)
PDF [652 KB, uploaded 25 August 2017]


In sensory swarms, minimizing energy consumption under performance constraint is one of the key objectives. One possible approach to this problem is to monitor application workload that is subject to change at runtime, and to adjust system configuration adaptively to satisfy the performance goal. As today’s sensory swarms are usually implemented using multi-core processors with adjustable clock frequency, we propose to monitor the CPU workload periodically and adjust the task-to-core allocation or clock frequency in an energy-efficient way in response to the workload variations. In doing so, we present an online heuristic that determines the most energy-efficient adjustment that satisfies the performance requirement. The proposed method is based on a simple yet effective energy model that is built upon performance prediction using IPC (instructions per cycle) measured online and power equation derived empirically. The use of IPC accounts for memory intensities of a given workload, enabling the accurate prediction of execution time. Hence, the model allows us to rapidly and accurately estimate the effect of the two control knobs, clock frequency adjustment and core allocation. The experiments show that the proposed technique delivers considerable energy saving of up to 45%compared to the state-of-the-art multi-core energy management technique. View Full-Text
Keywords: sensory swarm; energy minimization; multi-core processor; dynamic voltage frequency scaling (DVFS); self-adaptation; runtime resource management sensory swarm; energy minimization; multi-core processor; dynamic voltage frequency scaling (DVFS); self-adaptation; runtime resource management

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Kim , S.; Yang, H. An Energy-Aware Runtime Management of Multi-Core Sensory Swarms. Sensors 2017, 17, 1955.

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